Critical Questions Generation: Motivation and Challenges (2410.14335v1)

Abstract: The development of LLMs has brought impressive performances on mitigation strategies against misinformation, such as counterargument generation. However, LLMs are still seriously hindered by outdated knowledge and by their tendency to generate hallucinated content. In order to circumvent these issues, we propose a new task, namely, Critical Questions Generation, consisting of processing an argumentative text to generate the critical questions (CQs) raised by it. In argumentation theory CQs are tools designed to lay bare the blind spots of an argument by pointing at the information it could be missing. Thus, instead of trying to deploy LLMs to produce knowledgeable and relevant counterarguments, we use them to question arguments, without requiring any external knowledge. Research on CQs Generation using LLMs requires a reference dataset for large scale experimentation. Thus, in this work we investigate two complementary methods to create such a resource: (i) instantiating CQs templates as defined by Walton's argumentation theory and (ii), using LLMs as CQs generators. By doing so, we contribute with a procedure to establish what is a valid CQ and conclude that, while LLMs are reasonable CQ generators, they still have a wide margin for improvement in this task.

• The paper introduces a novel task of generating critical questions from argumentative text to uncover reasoning blind spots.
• It applies Walton’s argumentation schemes with both template instantiation and LLM-based generation to create and assess a comprehensive dataset.
• Results highlight LLM challenges in maintaining relevance and advocate a hybrid approach for enhancing critical questioning in misinformation mitigation.

Critical Questions Generation: Motivation and Challenges

The research paper "Critical Questions Generation: Motivation and Challenges" addresses the development of LLMs in the context of misinformation mitigation, specifically through the novel task of Critical Questions (CQs) Generation. This task involves processing argumentative text to generate the critical questions that reveal the underlying blind spots in the argument. The paper is grounded in argumentation theory, utilizing argumentation schemes as a framework and exploring two distinct methods for CQ generation: template instantiation and LLM-based generation.

Motivation and Approach

The motivation for this work stems from the limitations of LLMs in staying updated with factual knowledge and their propensity for content hallucination. Traditionally, LLMs have been employed to produce counterarguments, but this paper proposes an alternative: using LLMs to generate questions that identify the absence or weakness of claims, obviating the need for external knowledge.

The research investigates two methods for creating a dataset essential for CQ generation research:

1. Template Instantiation: Utilizing the critical questions templates defined by Walton’s argumentation theory.
2. LLM Generation: Leveraging LLMs as potential CQ generators.

The paper emphasizes the need to establish what constitutes a valid CQ and evaluates the effectiveness of LLMs in generating these critical inquiries.

Methodology

The paper begins by analyzing structured templates from Walton's argumentation theory to instantiate CQs, utilizing annotated argumentative texts. In parallel, state-of-the-art LLMs are prompted to generate potential CQs. A rigorous evaluation process is then applied to assess the relevance and validity of these LLM-generated questions. Specific attention is given to ensuring that CQs not only connect to the arguments within the text but also fulfill their core function of challenging the argument's acceptability.

Results and Implications

The findings reveal that LLMs can generate CQs, though they often struggle with maintaining relevancy and avoiding introducing new concepts not present in the original argument. There is a substantial difference between theory-based CQs and those generated by models, with LLM-generated CQs frequently focusing on evidential and definitional aspects not covered by traditional templates.

The results encourage a complementary approach wherein both theoretical templates and LLM-generated questions are used to build a comprehensive dataset for CQ generation. This hybrid approach is suggested to harness the strengths of each method and enhance the development and usability of CQs as a tool for critical thinking and fallacy identification.

Future Directions

The paper underscores the need for further refinement in the use of LLMs for generating CQs, advocating for enhanced training and prompting techniques. As LLMs continue to evolve, there is potential for these models to play a pivotal role in improving critical thinking applications and tools aimed at combating misinformation.

Moreover, the development of larger, more diverse reference datasets is essential, requiring efforts to annotate more argumentative data across various domains and languages. As the field progresses, the integration of LLMs in generating critical questions could prove invaluable in educational settings, fostering critical engagement with arguments across disciplines.

In conclusion, this research lays a foundational framework for future exploration in automating the generation of critical questions, pointing to the integration of AI and argumentation theory as a promising avenue for enhancing critical discourse analysis and misinformation mitigation practices.